Finding Optimal Surface for Hierarchical Classification Task on an Ontology

ABSTRACT

A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for findingoptimal surface for hierarchical classification task on an ontology.

Data intensive solutions, such as solutions that include machinelearning components, are becoming more and more prevalent. The standardway of developing such solutions is to train machine learning modelswith manually annotated or labeled data for a given task. Thismethodology assumes the existence of ample human annotated data.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided in a dataprocessing system having a processor and a memory. The memory comprisesinstructions which are executed by the processor to cause the processorto implement a training system for finding an optimal surface forhierarchical classification task on an ontology. The method comprisesreceiving, by the training system, a training data set and ahierarchical ontology data structure. The method further comprisesselecting, by a surface finding component executing within the trainingsystem, a surface that passes through each path from a root to a leafnode in the hierarchical ontology data structure. The method furthercomprises determining, by the surface finding component, a plurality ofadjacent surfaces that differ from the selected component by one node.The method further comprises selecting, by the surface findingcomponent, an optimal surface, based on the selected surface and theplurality of adjacent surfaces, that maximizes accuracy and coverage.The method further comprises training, by the training system, aclassifier model for a cognitive system using the optimal surface andthe training data set.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example diagram of a distributed data processing system inwhich aspects of the illustrative embodiments may be implemented;

FIG. 2 is an example block diagram of a computing device in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 is a block diagram of a training system including a surfacefinding component for finding an optimal surface for a hierarchicalclassification task on an ontology in accordance with an illustrativeembodiment;

FIGS. 4A-4D illustrate the trade-off between accuracy and leaf nodecoverage in accordance with an illustrative embodiment;

FIG. 5 depicts a three level labeling tree in accordance with anillustrative embodiment;

FIG. 6 is a flowchart illustrating operation of a training system forfinding optimal surface for hierarchical classification on an ontologyin accordance with an illustrative embodiment; and

FIG. 7 is a flowchart illustrating operation of a mechanism for findingthe optimal surface for hierarchical classification on an ontology inaccordance with an illustrative embodiment.

DETAILED DESCRIPTION

Data intensive solutions rely on data and on machine learning modelsthat generalize from this data. In many of the real-world problems, thequantity and the quality of the data challenge the machine learningalgorithms. In particular, the large-scale hierarchical classificationproblems are challenged by the data distribution and complexity of thehierarchy. This is prevalent in the medical domain where hierarchicalclassification tasks like coding the medical conditions to InternationalClassification of Disease (ICD) hierarchy, coding adverse events toMedical Dictionary for Regulatory Activities (MedDRA) hierarchy andcoding drug and compounds to World Health Organization Drug Dictionary(WHODD) hierarchy are essential for various purposes like diagnosing,billing, monitoring drug safety, secondary analysis tasks and regulatoryprocesses. The main challenges in solving these problems include:

-   -   Long tail distributions, making it infeasible to learn all the        data classes (labels), because there is insufficient training        data for classes appearing in the long tail.    -   Hierarchy of labels. There is a trade-off between the level of        label details (there are more details lower in the hierarchy)        and the amount of training data available (there is less data        lower in the hierarchy).    -   Often, the goal is to learn the most specific concept, i.e.,        cover the lowest level hierarchy classes. Of course, the        requirement is to do so with sufficiently high accuracy.

Given these challenges, it is important to understand the limitations ofthe machine learning models with respect to the quality and quantity ofthe available dataset. In a hierarchical classification problem, it maynot be possible to train a classifier that always classifies instancesto a leaf node. For example, if a particular leaf node has only a fewtraining examples or the existing examples are significantly diverse interms of their semantics, the classification algorithm may find itdifficult to learn to classify to this node. In such cases, it may makesense to train a classifier that is capable of classifying to the parentof that node. The goal of the illustrative embodiments is to find asurface in the hierarchy that maximizes a combination of: (1) theclassification accuracy; and, (2) coverage of leaf nodes in thehierarchy.

The illustrative embodiments provide a mechanism for determining acombination of nodes over which to train a cognitive system from anontology or a labeling hierarchy. The illustrative embodiments balanceaccuracy with class coverage by taking into account the fact that someclasses in the labeling hierarchy of the training data set do not haveenough training data or contain particularly difficult semantics whileother classes have great semantic diversity among the training data,e.g., the training dataset has a long tail distribution. The mechanismof the illustrative embodiments, given a set of training data for aclassification task, finds a surface in the labeling hierarchy, or line,that maximizes the accuracy of predictions and the coverage of specificclasses in the hierarchy.

Prior art solutions determine which classes can or cannot be used totrain a cognitive system. The prior art solutions select the classesthat can be trained and ignore other classes. In contrast, the mechanismof the illustrative embodiments does not ignore the classes. Themechanism of the illustrative embodiments determines the abstractnesslevel, expressed as a surface or line in the hierarchy, that can be usedto train the classification model or algorithm.

Before beginning the discussion of the various aspects of theillustrative embodiments, it should first be appreciated that throughoutthis description the term “mechanism” will be used to refer to elementsof the present invention that perform various operations, functions, andthe like. A “mechanism,” as the term is used herein, may be animplementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The illustrative embodiments may be utilized in many different types ofdata processing environments. In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 1 and 2 are provided hereafter asexample environments in which aspects of the illustrative embodimentsmay be implemented. It should be appreciated that FIGS. 1 and 2 are onlyexamples and are not intended to assert or imply any limitation withregard to the environments in which aspects or embodiments of thepresent invention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIG. 1 depicts a pictorial representation of an example distributed dataprocessing system in which aspects of the illustrative embodiments maybe implemented. Distributed data processing system 100 may include anetwork of computers in which aspects of the illustrative embodimentsmay be implemented. The distributed data processing system 100 containsat least one network 102, which is the medium used to providecommunication links between various devices and computers connectedtogether within distributed data processing system 100. The network 102may include connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 are connected tonetwork 102 along with storage unit 108. In addition, clients 110, 112,and 114 are also connected to network 102. These clients 110, 112, and114 may be, for example, personal computers, network computers, or thelike. In the depicted example, server 104 provides data, such as bootfiles, operating system images, and applications to the clients 110,112, and 114. Clients 110, 112, and 114 are clients to server 104 in thedepicted example. Distributed data processing system 100 may includeadditional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 100 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 1 is intended as anexample, not as an architectural limitation for different embodiments ofthe present invention, and therefore, the particular elements shown inFIG. 1 should not be considered limiting with regard to the environmentsin which the illustrative embodiments of the present invention may beimplemented.

As shown in FIG. 1, one or more of the computing devices, e.g., server104, may be specifically configured to implement a classifier trainingsystem with a surface finding component that finds the optimal surfacefor a hierarchical classification on an ontology. The configuring of thecomputing device may comprise the providing of application specifichardware, firmware, or the like to facilitate the performance of theoperations and generation of the outputs described herein with regard tothe illustrative embodiments. The configuring of the computing devicemay also, or alternatively, comprise the providing of softwareapplications stored in one or more storage devices and loaded intomemory of a computing device, such as server 104, for causing one ormore hardware processors of the computing device to execute the softwareapplications that configure the processors to perform the operations andgenerate the outputs described herein with regard to the illustrativeembodiments. Moreover, any combination of application specific hardware,firmware, software applications executed on hardware, or the like, maybe used without departing from the spirit and scope of the illustrativeembodiments.

It should be appreciated that once the computing device is configured inone of these ways, the computing device becomes a specialized computingdevice specifically configured to implement the mechanisms of theillustrative embodiments and is not a general purpose computing device.Moreover, as described hereafter, the implementation of the mechanismsof the illustrative embodiments improves the functionality of thecomputing device and provides a useful and concrete result thatfacilitates finding an optimal surface for a hierarchical classificationon an ontology.

As noted above, the mechanisms of the illustrative embodiments utilizespecifically configured computing devices, or data processing systems,to perform the operations for an optimal surface finding component.These computing devices, or data processing systems, may comprisevarious hardware elements which are specifically configured, eitherthrough hardware configuration, software configuration, or a combinationof hardware and software configuration, to implement one or more of thesystems/subsystems described herein. FIG. 2 is a block diagram of justone example data processing system in which aspects of the illustrativeembodiments may be implemented. Data processing system 200 is an exampleof a computer, such as server 104 in FIG. 1, in which computer usablecode or instructions implementing the processes and aspects of theillustrative embodiments of the present invention may be located and/orexecuted so as to achieve the operation, output, and external effects ofthe illustrative embodiments as described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBMeServer™ System p® computer system, Power™ processor based computersystem, or the like, running the Advanced Interactive Executive (AIX®)operating system or the LINUX® operating system. Data processing system200 may be a symmetric multiprocessor (SMP) system including a pluralityof processors in processing unit 206. Alternatively, a single processorsystem may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

As mentioned above, in some illustrative embodiments the mechanisms ofthe illustrative embodiments may be implemented as application specifichardware, firmware, or the like, application software stored in astorage device, such as HDD 226 and loaded into memory, such as mainmemory 208, for executed by one or more hardware processors, such asprocessing unit 206, or the like. As such, the computing device shown inFIG. 2 becomes specifically configured to implement the mechanisms ofthe illustrative embodiments and specifically configured to perform theoperations and generate the outputs described hereafter with regard tothe classifier training system with a surface finding component thatfinds the optimal surface for a hierarchical classification on anontology.

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 1 and 2 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 1 and 2. Also,the processes of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thepresent invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 is a block diagram of a training system including a surfacefinding component for finding an optimal surface for a hierarchicalclassification task on an ontology in accordance with an illustrativeembodiment. Training system 310 receives a training data set 302 fortraining a classifier model 311 on an ontology or labeling hierarchy 301for cognitive system 320. Under control of classifier model 311,cognitive system 320 classifies data into classes in ontology 301.Cognitive system 320 may perform a cognitive operation, such as naturallanguage processing, question answering, decision support, etc. In oneexample, cognitive system 320 may perform a hierarchical classificationtask to classify training data based on an ontology in the medicaldomain.

Training system 310 includes surface finding component 310 for findingthe optimal surface for hierarchical classification tasks on ontology301. For example, a classification task may seek to classify an adverseevent (AE) to a particular medical code; however, the training data 302may have some codes that have a large amount of training data and othersthat do not have very much training data. A long tail distribution is atraining data set in which there are many training instances for someclasses, but the number of training instances decreases rapidly andthere are much fewer training instances for a majority of the classes inthe ontology. Therefore, the classifier model 311 can be trained withgreater specificity for the classes with the most training instances intraining data set 302, but the classifier model 311 can be trained at ahigher level for the classes with fewer training instances. Whentraining the classifier model 311 for cognitive system 320, given a longtail distribution, the problem is how to get a good training data set totrain the cognitive system to perform an accurate classification whileincluding as many leaf nodes in the hierarchy as possible. Clients usingthe cognitive system 320 for classification want a cognitive system thatis trained on as many leaf nodes as possible as they represent morespecific classification results, but there may be insufficient trainingdata for accurately classifying into these classes. Thus, inclusion ofleaf nodes tends to reduce accuracy.

The illustrative embodiments define a surface over a hierarchy to be aset of hierarchy tree nodes that intersect once each path from the rootof the hierarchy (highest level node) to the leaves of the hierarchy(lowest level nodes). FIGS. 4A-4D illustrate the trade-off betweenaccuracy and leaf node coverage in accordance with an illustrativeembodiment. The hierarchy 400 has three levels. The ideal scenario wouldbe that the training data set contains sufficient data with high qualityto train a classifier that can classify new instances to one of theseven leaf nodes. However, when the training data does not satisfy theabove requirement, it is important to decompose the problem and solvesub-problems.

The goal of the illustrative embodiments is to find the segment of thehierarchy 400 where the training system can train the model to classifyat leaf node level and the segment where the training system has torestrict the classification problem to higher level in the hierarchy400. This segmentation is indicated with a line drawn on the hierarchyreferred to herein as the “surface.” However, the training system shouldkeep a balance when drawing the surface on the hierarchy. As shown inthe FIG. 4A, as the surface 51 is moved up, the classifier model canpotentially get a higher accuracy but may lose the specificity of theclassification results. Hence, it is important to cover as many leafnodes as possible while attaining the desired accuracy level.

As seen in FIG. 4A, the number of leaf nodes through which surface 51passes is two. Turning to FIG. 4B, the number of leaf nodes throughwhich surface S2 passes is three; however, note that surface S2 passesthrough each class or each path from the root to a given leaf Withreference now to FIG. 4C, surface S3 passes through each class ofhierarchy 400 but only passes through one leaf node. In FIG. 4D, surfaceS4 passes through five leaf nodes. Thus, surface S3 in FIG. 4C mayachieve more accurate results with less specific classification for mostclasses, but surface S4 may cover more leaf nodes for more specificresults but may sacrifice accuracy.

The defined optimization problem balances the need for accuracy and thecoverage of leaf nodes. The mechanism starts with a surface andcalculates the accuracy and the number of leaf nodes covered, andoptimizes over these characteristics. The task of classifying to theleaves that are not included in the surface should be handledseparately. A possible approach is to implement a non-learning solutionfor these leaf classes (e.g., a dictionary). Another possible approachis to implement a learning solution, where there is only a need toclassify the nodes under the chosen surface node. For those leaf nodesof the hierarchy not included in the surface, the mechanism determinesthe correct parent class for the data instance. This allows the trainingsystem to train a separate classifier for leaf nodes under this parentto determine their leaf node. Note that this classifier is easier totrain relative to the large classifier and would have less impact fromthe long-tailed data distribution due to a significantly smaller numberof classes.

In one embodiment, a user may specify a number of leaf nodes to beincluded in the training data set and the accuracy desired for theclassification operation. From these inputs, it is possible to determinethe surface that provides the desired number of leaf nodes and accuracyfor use in selecting training data. The method optimizes the accuracyplus the highest number of nodes. Additional optimization goals may beadded, such as a requirement for high diversity in the content (e.g.,textual description) of the leaf nodes.

The illustrative embodiments provide a mechanism for determining acombination of nodes over which to train a machine learning model froman ontology or a labeling hierarchy. Any proposed algorithm must balancelearning accuracy with leaf nodes coverage. Various considerationsexist. For example, some classes in the labeling hierarchy of thetraining data set do not have enough training data or containparticularly difficult semantics while instances of another class havehomogeneous semantic in the training data. In this case, it might makesense to draw the surface covering the latter class and the parent ofthe former class.

The illustrative embodiments define the following search problem. Givena set of training data for a classification task, the goal is to find asurface in the labeling hierarchy that maximizes the accuracy ofpredictions and the coverage of specific classes in the hierarchy.

The mechanism is given a training set (X₁, Y₁), . . . (X_(n), Y_(n)).The mechanism is also given a labeling hierarchy T. Each Y_(i)corresponds to a path in T from root to leaf A labeling surface, s, is aset of tree nodes that intersect each path from the root to the leafonce. For example, FIG. 5 depicts a three level labeling tree inaccordance with an illustrative embodiment:

T={(R, P1), (R, P2), (P1, C1), (P1, C2), (P1, C3), (P2, C4), (P2, C5)}

s=(C1, C2, C3, P2)

Where T is a label tree and s is a possible surface as depicted by theline over the tree nodes.

Given a labeling surface s, the mechanism relabels the training setusing the surface s as follows. The new label of X₁ is defined as theintersection between the surface s and Y_(ti),_(Y) ^(s) ^(i) =s∩Y_(i).The learning problem associated with surface s is L_(s). The mechanismapplies some learning algorithm to L_(s) and gets an accuracy valuef(L_(s)). The mechanism trains on re-labelled training data according tothe selected surface and evaluates the resulting cognitive model with atesting dataset. The mechanism compares the accuracy value generated foreach surface. For a given surface s, an adjacent surface s′ is a surfacethat differs from s by only one node. The set of all such adjacentsurfaces of s is denoted as adj(s).

Given a surface s the mechanism defines its weight to be the sum ofdepth of all its nodes, denoted by w(s). For example, in the depictedexample the weight is w(s)=2+2+2+1. Now the following discrete gradientdecent greedy search algorithm is defined. Set m to be a weighted hyperparameter of the algorithm.

1. Choose some surface s.

2. Calculate C(s)=f(L_(S))+m*w(s) for s and any s∈adj (s). Pick s thatmaximizes C(s), where C(s) quantifies a balance between accuracy andleaf node coverage.

3. Repeat until C(s) does not improve by t, where t is a predefinedthreshold value.

FIG. 6 is a flowchart illustrating operation of a training system forfinding optimal surface for hierarchical classification on an ontologyin accordance with an illustrative embodiment. Operation begins (block600), and the training system receives a training data set and ahierarchical labeling tree (block 601). The training system receives aspecified number of leaf nodes to be included in the training data setand the accuracy desired (block 602). A surface finding component withinthe training system finds a surface that provides the desired number ofleaf nodes and accuracy for use in training the classifier model (block603). Operation for finding the optimal surface for hierarchicalclassification on an ontology is described in further detail below withreference to FIG. 7. Then, the training system trains the classifiermodel for the cognitive system using the training data set based on theselected surface (block 604). Thereafter, operation ends (block 605).The training system may determine whether the optimal surface found inblock 603 covers the desired number of leaf nodes and achieves thedesired accuracy.

The training dataset is re-labeled according to the selected surface.Then any classification algorithm is selected and this training data isused to train a model, i.e., the training example (x, y), where x is theinput and y is the correct label for x, is given to the classifier andit is trained to minimize the difference between the predicted label forx and y. In this step, classifier specific operations, such as featureselection, feature normalization, hyperparameter selection tuning, maybe performed.

FIG. 7 is a flowchart illustrating operation of a mechanism for findingthe optimal surface for hierarchical classification on an ontology inaccordance with an illustrative embodiment. Operation begins (block700), and the mechanism selects a labeling surface (block 701). In oneembodiment, this initial surface may be chosen randomly. In anotherembodiment, the initial surface may be chosen by selecting a linethrough a predetermined level of the hierarchy. For example, for athree-level hierarchy, such as the examples in FIGS. 4A-4D and FIG. 5,the initial surface may pass through each path at the second level. Inanother example embodiment, the initial surface may be chosen to includethe number of leaf nodes specified in block 602 in FIG. 6.

The mechanism then relabels the training set using the selected surface(block 702). The formal description is of relabeling the training set isabove. Intuitively, what it does is take the training example (x,y), ifthe y is a node that is included in the selected surface, then themechanism does nothing; otherwise, the mechanism checks the parent of ythat is included in the surface, assume that is yp, then the mechanismrelabels the training instance to (x, yp). The mechanism then determinesadjacent surfaces (block 703). Each adjacent surface differs from theselected surface by one node. The mechanism relabels the training setusing each of the adjacent surfaces (block 704). The mechanism thencalculates the accuracy and coverage for the surface and adjacentsurfaces (block 705). The mechanism chooses the surface that maximizesaccuracy and coverage (block 706).

The mechanism then determines whether the accuracy and coverage of thechosen surface improves significantly for an adjacent surface comparedto the current surface (block 707). If the accuracy and coverageimprove, then the adjacent surface is chosen, and operation returns toblock 701 to determine a next surface. In other words, the mechanismcalculates the C(s) for each adjacent surface at 705. If the differencebetween C(s) of any adjacent surface and C(s) of the selected surface atblock 701 is greater than t, a predetermined threshold, then themechanism selects that adjacent surface and starts again from block 701.Operation repeats until accuracy and coverage do not improve in block707, in which case operation ends (block 708).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, bulk storage, and cachememories which provide temporary storage of at least some program codein order to reduce the number of times code must be retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid state memory, and the like.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters for wired communications.Wireless communication based network adapters may also be utilizedincluding, but not limited to, 802.11 a/b/g/n wireless communicationadapters, Bluetooth wireless adapters, and the like. Any known or laterdeveloped network adapters are intended to be within the spirit andscope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

1-20. (canceled)
 21. A method, in a data processing system having aprocessor and a memory, wherein the memory comprises instructions whichare executed by the processor to cause the processor to implement atraining system for finding an optimal surface for hierarchicalclassification task on an ontology, the method comprising: receiving, bythe training system, a training data set and a hierarchical ontologydata structure; receiving, by the training system, a number of leafnodes to be covered and a desired accuracy; identifying, by a surfacefinding component executing within the training system, a plurality ofsurfaces that pass through each path from a root to a leaf node in thehierarchical ontology data structure; selecting, by the surface findingcomponent, an optimal surface within the plurality of adjacent surfacesthat covers the number of leaf nodes, and achieves the desired accuracy;and training, by the training system, a classifier model for a cognitivesystem on re-labelled training data using the optimal surface and thetraining data set.
 22. The method of claim 21, wherein selecting theoptimal surface comprises: selecting an initial surface that passesthrough each path from a root to a leaf node in the hierarchicalontology data structure; determining a plurality of adjacent surfacesthat differ from the selected surface by one node; determining acombined coverage and accuracy value for the selected surface and foreach of the plurality of adjacent surfaces; and determining whether acombined coverage and accuracy value of a given adjacent surface isgreater than a combined coverage and accuracy value of the selectedsurface by a predetermined threshold.
 23. The method of claim 22,wherein selecting the optimal surface further comprises: responsive todetermining the combined coverage and accuracy value of the givenadjacent surface is greater than the combined coverage and accuracyvalue of the selected surface by the predetermined threshold, selectingthe given adjacent surface as the selected surface and repeatingdetermining a plurality of adjacent surfaces that differ from theselected component by one node and selecting an optimal surface, basedon the selected surface and the plurality of adjacent surfaces, thatmaximizes accuracy and coverage.
 24. The method of claim 22, whereinselecting the optimal surface further comprises: responsive todetermining that there is no given adjacent surface having a combinedcoverage and accuracy value that is greater than the combined coverageand accuracy value of the selected surface, identifying the selectedsurface as the optimal surface.
 25. The method of claim 21, wherein thecognitive system classifies data into classes in the hierarchicalontology data structure under control of the classifier model.
 26. Themethod of claim 25, wherein the cognitive system classifies to leafnodes that are not included in the surface separately.
 27. The method ofclaim 21, wherein training the classifier comprises evaluating aresulting cognitive model with a testing dataset.
 28. A computer programproduct comprising a computer readable storage medium having a computerreadable program stored therein, wherein the computer readable program,when executed on at least one processor of a computing device, causesthe at least one processor to implement a training system for finding anoptimal surface for hierarchical classification task on an ontology,wherein the computer readable program causes the at least one processorto: receive, by the training system, a training data set and ahierarchical ontology data structure; receiving, by the training system,a number of leaf nodes to be covered and a desired accuracy, identify,by a surface finding component executing within the training system, aplurality of surfaces that pass through each path from a root to a leafnode in the hierarchical ontology data structure; select, by the surfacefinding component, an optimal surface within the plurality of adjacentsurfaces, that covers the number of leaf nodes, and achieves the desiredaccuracy; and train, by the training system, a classifier model for acognitive system on re-labelled training data using the optimal surfaceand the training data set.
 29. The computer program product of claim 28,wherein selecting the optimal surface comprises: selecting an initialsurface that passes through each path from a root to a leaf node in thehierarchical ontology data structure; determining a plurality ofadjacent surfaces that differ from the selected surface by one node;determining a combined coverage and accuracy value for the selectedsurface and for each of the plurality of adjacent surfaces; anddetermining whether a combined coverage and accuracy value of a givenadjacent surface is greater than a combined coverage and accuracy valueof the selected surface by a predetermined threshold.
 30. The computerprogram product of claim 29, wherein selecting the optimal surfacefurther comprises: responsive to determining the combined coverage andaccuracy value of the given adjacent surface is greater than thecombined coverage and accuracy value of the selected surface by thepredetermined threshold, selecting the given adjacent surface as theselected surface and repeating determining a plurality of adjacentsurfaces that differ from the selected component by one node andselecting an optimal surface, based on the selected surface and theplurality of adjacent surfaces, that maximizes accuracy and coverage.31. The computer program product of claim 29, wherein selecting theoptimal surface further comprises: responsive to determining that thereis no given adjacent surface having a combined coverage and accuracyvalue that is greater than the combined coverage and accuracy value ofthe selected surface, identifying the selected surface as the optimalsurface.
 32. The computer program product of claim 28, wherein thecognitive system classifies data into classes in the hierarchicalontology data structure under control of the classifier model.
 33. Thecomputer program product of claim 32, wherein the cognitive systemclassifies to leaf nodes that are not included in the surfaceseparately.
 34. The computer program product of claim 28, whereintraining the classifier comprises evaluating a resulting cognitive modelwith a testing dataset.
 35. An apparatus comprising: at least oneprocessor, and a memory coupled to the at least one processor, whereinthe memory comprises instructions which, when executed by the at leastone processor, cause the at least one processor to implement a trainingsystem for finding an optimal surface for hierarchical classificationtask on an ontology, wherein the instructions cause the at least oneprocessor to: receive, by the training system, a training data set and ahierarchical ontology data structure; receive, by the training system, anumber of leaf nodes to be covered and a desired accuracy; identify, bya surface finding component executing within the training system, aplurality of surfaces that pass through each path from a root to a leafnode in the hierarchical ontology data structure; select, by the surfacefinding component, an optimal surface within the plurality of adjacentsurfaces that covers the number of leaf nodes and achieves the desiredaccuracy; and train, by the training system, a classifier model for acognitive system on re-labelled training data using the optimal surfaceand the training data set.
 36. The apparatus of claim 35, whereinselecting the optimal surface comprises: selecting an initial surfacethat passes through each path from a root to a leaf node in thehierarchical ontology data structure; determining a plurality ofadjacent surfaces that differ from the selected surface by one node;determining a combined coverage and accuracy value for the selectedsurface and for each of the plurality of adjacent surfaces; anddetermining whether a combined coverage and accuracy value of a givenadjacent surface is greater than a combined coverage and accuracy valueof the selected surface by a predetermined threshold.
 37. The apparatusof claim 36, wherein selecting the optimal surface further comprises:responsive to determining the combined coverage and accuracy value ofthe given adjacent surface is greater than the combined coverage andaccuracy value of the selected surface by the predetermined threshold,selecting the given adjacent surface as the selected surface andrepeating determining a plurality of adjacent surfaces that differ fromthe selected component by one node and selecting an optimal surface,based on the selected surface and the plurality of adjacent surfaces,that maximizes accuracy and coverage.
 38. The apparatus of claim 36,wherein selecting the optimal surface further comprises: responsive todetermining that there is no given adjacent surface having a combinedcoverage and accuracy value that is greater than the combined coverageand accuracy value of the selected surface, identifying the selectedsurface as the optimal surface.
 39. The apparatus of claim 35, whereinthe cognitive system classifies data into classes in the hierarchicalontology data structure under control of the classifier model.
 40. Theapparatus of claim 39, wherein the cognitive system classifies to leafnodes that are not included in the surface separately.